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trainEfficientADAnomalyDetector

Train EfficientAD anomaly detection network

Since R2024b

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    Syntax

    detector = trainEfficientADAnomalyDetector(normalData,detectorIn,options)
    detector = trainEfficientADAnomalyDetector(___,Ratio=MapNormalizationDataRatio)

    Description

    detector = trainEfficientADAnomalyDetector(normalData,detectorIn,options) trains the input EfficientAD anomaly detection network detectorIn. The training data consists of the specified normal images normalData.

    Note

    This functionality requires Deep Learning Toolbox™ and the Automated Visual Inspection Library for Computer Vision Toolbox™. You can install the Automated Visual Inspection Library for Computer Vision Toolbox from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

    Note

    It is recommended that you also have Parallel Computing Toolbox™ to use with a CUDA® enabled NVIDIA® GPU. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).

    detector = trainEfficientADAnomalyDetector(___,Ratio=MapNormalizationDataRatio) specifies the data ratio to use for anomaly map normalization, in addition to the input arguments from the previous syntax.

    Input Arguments

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    EfficientAD anomaly detector to train, specified as an efficientADAnomalyDetector object.

    Training data, specified as a datastore. The training data consists of samples of normal images. Do not include anomaly images in the training data.

    Training options, specified as a TrainingOptionsSGDM, TrainingOptionsRMSProp, or TrainingOptionsADAM object returned by the trainingOptions (Deep Learning Toolbox) function. To specify the solver name and other options for network training, use the trainingOptions function.

    Tip

    When you perform tile-based training and GPU memory is limited, reduce the mini-batch size using the MiniBatchSize (Deep Learning Toolbox) argument of the trainingOptions function, at the expense of slower computation speed.

    Data ratio to use for anomaly map normalization, specified as a numeric scalar in the range [0, 1]. For example, a data ratio of 0.1 specifies that the detector must use only 10% of the training data for anomaly map normalization. To not perform the normalization, set the value to 0. Use a higher percentage of training data for normalization when your training data size is large.

    Output Arguments

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    Trained EfficientAD anomaly detector, returned as an efficientADAnomalyDetector object.

    Tips

    • For a given training image size and number of training images, if the peak memory usage for creating a memory bank exceeds the available memory, EfficientAD outputs a warning. To address insufficient memory, try reducing image resolution, using fewer training images, or enhancing GPU performance.

    Version History

    Introduced in R2024b

    See Also

    | | (Deep Learning Toolbox)

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